A scalable activity-based and physics-informed bottom-up model for residential electricity demand using open data

• An activity-based, physics-informed bottom-up model for residential electricity demand is developed. • The framework relies exclusively on open data for construction, calibration and validation. • Physics-based appliance models enable explicit representation of energy transition pathways and climate effects. • A hierarchical clustering strategy ensures scalability and preserves spatial heterogeneity. • The approach reconstructs aggregated load profiles accurately over large geographical areas. In the context of the ongoing energy transition, accurately modeling the spatial and temporal heterogeneity of residential electricity load curves has become increasingly critical. Such models are essential for prospective studies ranging from emerging technology adoption to multi-scale distribution system analyses. This paper introduces an activity-based, physics-informed bottom-up model of residential electricity demand, developed exclusively from open data for its construction, calibration, and validation. The activity-based structure enables a realistic representation of occupant behavior, while the physics-based analytical expressions allow for the explicit modeling of transition pathways as well as the impact of climate change. To ensure scalability over large geographical domains, a hierarchical clustering methodology is employed to identify a minimal yet representative subset of districts. This approach enables the reconstruction of aggregated load profiles across extensive regions while preserving computational efficiency and accuracy. The resulting framework constitutes a scalable, parametrizable and robust tool for electricity consumption modeling that can be leveraged in a wide range of energy transition prospective studies.